The first category related this work is optimistic exploration which is widely used in RL. Upper-Confidence-Bound exploration [1]} used in AlphaGo is similar to greedy selection. They both tend to choose the latest or best actions. The optimistic inital value [2]} realizes exploration by increasing the initial value of the function, which essentially explores the state with a lower frequency of occurrence. It is worth noting that selection of initial values requires the prior knowledge, and the exploration will be unstable during the initial stage. Agents tend to selcet actions with higher entropy values in the gradient bandit algorithm [3]}, where the entropy of each action is adjusted by the rewards.
